package org.example
import org.apache.spark.sql.SparkSession
object x46u {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().master("local[*]").appName("spark").getOrCreate()
    val sc = spark.sparkContext
    val first_half = sc.textFile("E:\\Employee_salary_first_half.csv")
    val second_half = sc.textFile("E:\\Employee_salary_second_half.csv")
    val drop_first = first_half.mapPartitionsWithIndex((ix, it) => {
      if (ix == 0) it.drop(1)
      it
    })
    val drop_second = second_half.mapPartitionsWithIndex((ix, it) => {
      if (ix == 0) it.drop(1)
      it
    })
    val split_first = drop_first.map {
      line => {
        val data = line.split(",")
        try {
          (data(1), data(6).toInt)
        } catch {
          case e: NumberFormatException => (data(1), 0)
        }
      }
    }
    val split_second = drop_second.map {
      line => {
        val data = line.split(",")
        try {
          (data(1), data(6).toInt)
        } catch {
          case e: NumberFormatException => (data(1), 0)
        }
      }
    }
    val filter_first = split_first.filter(x => x._2 > 200000).map(x => x._1)
    val filter_second = split_second.filter(x => x._2 > 200000).map(x => x._1)
    val name = filter_first.union(filter_second).distinct()
    name.collect().foreach(println)
    val salary = split_first.union(split_second)
    val avg_salary = salary.combineByKey(
      count => (count, 0),
      (acc: (Int, Int), count) => (acc._1 + count, acc._2 + 0),
      (acc1: (Int, Int), acc2: (Int, Int)) => (acc1._1 + acc2._1, acc1._2 + acc2._2)
    )
    avg_salary.map(x => (x._1, x._2._1.toDouble / 12)).foreach(println)

    val total =
      split_first.join(split_second).join(salary).join(avg_salary).map(
        x => Array(x._1, x._2._1._1._1, x._2._1._1._2,
          x._2._1._2, x._2._2).mkString(",")
      )
    total.repartition(1).saveAsTextFile("E:/1/save")
    sc.stop()
  }
}
